OMEGA Release 2.5.1.4 OpenEye Scientific Software, Inc.

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OMEGA, Release 2.5.1.4 Finally, you should eliminate the types of compounds that can be troublesome at later stages. For instance, Shoichet’s aggregating compounds often produce false positives that can waste enormous resources if they were identified by virtual or high-throughput screening [McGovern-2003] [Seidler-2003] . Similarly, dyes can appear to be inhibitors by interfering with colorimetric or fluorometric assays or binding non-specifically to the target protein. 8.1.4 Variations of Filters Different types of filters are appropriate under different circumstances. Very early in a project, when little or no SAR is available, very strict drug-like filters can be applied. This prevents a project team from spending chemistry resources pursuing difficult compounds that may not be modifiable to introduce appropriate properties. However, when considering compounds for purchase for HTS, different filters can be applied. Oprea, et al, pointed out that the best molecules for initial HTS are smaller and less functionalised than drugs, but with some activity [Oprea-2000]. Therefore, strict lead-like filters can be applied to ensure that hits identified from HTS have sufficient “room” for elaboration into (usually larger and more highly functionalised) leads. However, when SAR suggests that particular compounds or series may yield valuable information, filtering criteria can be loosened, because the secondary screens (QSAR models, similarity to known actives) that are being applied are effective in detecting useful compounds. Reflecting back on the medical analogy, this is the case where an improved primary screen with a dramatically improved false-positive rate (say 1 in 100,000) can be safely applied to a larger population without terrible effects on the positive-predictive value. FILTER provides the following “light” (lenient) and “heavy” (restrictive) filters: • BlockBuster • Lead The BlockBuster filter is based on 141 best-selling, non-antibiotic, prescription drugs. We designed the physical property portion of the filter so that it passes all of the compounds. The physical property values in this filter are quite good. However, the functional group filters in this filter are probably too restrictive because 141 compounds is not sufficient to span all acceptable functionality. Note: The original [Oprea-2000] Drug filter is provided as well. However, experience has shown it to be too restrictive. The BlockBuster filter was developed in response to complaints about the Drug filter being too restrictive. Hint: We recommend the BlockBuster filter, the default, for most purposes. If your project is unusual, or you are unsatisfied with the results we recommend you review the filter file for your specific filtering needs. If you decide to modify the filter file the depictions found in the Functional Group Rules section can be particularly helpful in determining what functional groups are indicated by each name in the file. 8.1.5 Accumulation of Rules FILTER contains numerous rules that judge the quality of molecules on many different facets. When examined individually, each of these rules seems quite reasonable and even profitable. However, when each molecule is tested against hundreds of individual filters, the fraction of molecules that pass all the filters can be surprisingly small. Sometimes less than 50% of vendor databases pass the filters. If this is unacceptable we recommend you examine the predicted aggregator, solubility, and Veber filters. In our experience, these are the most common failures. The best method of investigating failures is looking at the filter log. The BlockBuster filter was adjusted to demonstrate this in a tangible way. For each value, rather than spanning the entire range, its properties were set to cover from the 2.5th percentile to the 97.5th percentile. The differences between the original BlockBuster filter and the adjusted filter are both in reasonable ranges. For instance, the full range of molecular weight for the BlockBuster filter spans 130 to 781, while the 2.5th percentile is 145 and the 97.5th percentile is 570. The remarkable result is that when the reduced filter is used, only 75 of the 141 original molecules pass the filter! This demonstrates how slight changes to many filters can lead to a significant reduction in the number of compounds that pass all of the filters. 34 Chapter 8. Filtering Theory
OMEGA, Release 2.5.1.4 Taking the opposite approach of allowing everything to pass can be equally futile. To demonstrate this a filter was designed around the “small molecule drug” file available from the DrugBank website. In order to pass every one of these molecules, including some that would not be acceptable for modern project work, many of the individual filters must be set to unreasonable values. For instance, the molecular weight range is 30 to 1500 and the hetero-atom count range is 1 to 60! Hint: OpenEye can not magically divine the needs of every project. You should personally inspect the filter file. The depictions found in the Functional Group Rules section can be particularly helpful in discerning what functional groups are desirable and undesirable. 8.1. Introduction 35
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